Improved Search Algorithm of Digital Speckle Pattern Based on PSO and IC-GN
Round 1
Reviewer 1 Report
This paper presents an improved processing scheme for digital speckle pattern, which may draw attentions from the imaging engineers. However, the current version of the presented article is not well organized as a research paper and somewhat long.
- The presented article for the part, e.g., (sections 2.1 - 2.3) has the similar description of the article:
Wu, R., Kong, C., Li, K. et al. Real-Time Digital Image Correlation for Dynamic Strain Measurement. Exp Mech 56, 833–843 (2016). https://doi.org/10.1007/s11340-016-0133-6.
- In section 2.3, devoted to the searching algorithm, the authors may be expected to bring more new knowledge in the field of searching scheme.
- The presented article in section 4.2-4.3, use an ambiguous description for result comparisons, it is hard to follow the context and there were no corresponding indicators in the drawings. Maybe tables and a brief description style can be used.
- Another point to mention is the abbreviation PSO, it seemed that no comment on it in the article.
For the above reasons, I do not recommend publishing the current version of the article in the journal.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper proposes an improved digital speckle correlation method, which consists of an integer-pixel search algorithm and a sub-pixel search algorithm. The results show that the proposed algorithm has higher accuracy and higher efficiency than the comparison algorithm. Overall, the paper is well written. However, these concerns should be addressed before accepting for publication.
- DIC method has a wide application in deformation measurements. Together with the research works mentioned in the introduction, It is also to include some related works in different journals to attract more readers who are working on DIC such as Material science ( https://doi.org/10.1016/j.ijfatigue.2017.02.020 ); Biology ( https://doi.org/10.1088/1748-3182/6/4/046003 ); Aerospace (https://doi.org/10.1007/s11340-015-9987-2 )
- Why did the authors used the zero-mean normalized cross-correlation function CZNCC and zero-mean normalized difference square sum correlation function CZNSSD since there are some correlation functions such as Zero-normalized cross-correlation (ZNCC) function, Normalized cross-correlation (NCC) Cross-correlation (CC) function, Sum of squared differences (SSD) function. Please specify clearly in section 2.1
- It is better to include the full-field measurement results of the glass due to the movement
- It is interesting to compare the proposed method to other free 2D DIC software such ARAMIS 2D from GOM
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
In this article, the authors proposed two developed algorithms of Digital Image Correlation based on integer-pixel search and sub-pixel search, respectively. They clearly discussed and established the introduction part where described the features of all modern approaches, namely, their advantages and drawbacks. However, I would like to recommend authors answer the following points and comments:
1) Introduction part looks well-written, but it lost the valuable part - need to add the aim of this article.
2) Red lines look invisible/illegible. Please, correct this.
3) What programing language did you use during the algorithm development? Are these algorithms private, or can you share them with the audience? If yes, please, mention some resources for this purpose (git, cloud, etc.).
4) How can you increase the computational efficiency of your algorithms? Did you compare time-consuming with others?
5) Is it possible to write these algorithms to perform computation in a parallel way with MPI, for example? What kind of tricks could you offer for this goal?
6) In lines 124-126, you mentioned a rectangular area of 5x5 or 3x3 pixels. Why did you choose these values? Why is the shape rectangular but not another?
7) Please, add physical size (scale bar) for images in Figures 3, 6, 13.
8) Why did you choose the quadric surface fitting function?
9) Please, extend your conclusion and support it with more results. Maybe, it would be better to create a list with achieved advantages.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
No additional comments on the manuscript now.
Reviewer 2 Report
The manuscript was significantly improved after the revision. It can be accepted for publication.
Reviewer 3 Report
Well done. Good luck with the next investigations.
